Incrementing Multi-objective Evolutionary Algorithms: Performance Studies and Comparisons

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1993)


This paper addresses the issue by presenting a novel “incrementing” multi-objective evolutionary algorithm (IMOEA) with dynamic population size that is adaptively computed according to the on-line discovered trade-off surface and its desired population distribution density. It incorporates the method of fuzzy boundary local perturbation with interactive local fine-tuning for broader neighborhood exploration to achieve better convergence as well as discovering any gaps or missing trade-off regions at each generation. Comparative studies with other multi-objective (MO) optimization are performed on benchmark problem. The new suggested quantitative measures together with other well-known measures are employed to access and compare their performances statistically.


Genetic Algorithm Multiobjective Optimization Objective Domain Multiobjective Evolutionary Algorithm Algorithm Effort 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  1. 1.Department of Electrical and Computer EngineeringNational University of SingaporeCrescentSingapore

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